Die Einführung des GKV-FQWG sorgt für einen verstärkten Wettbewerbsdruck innerhalb des Systems der gesetzlichen Krankenkassen. Bestehen hohe Anreize zur Risikoselektion, so kann dieser Druck in einen vermehrten Kampf um vermeintlich vorteilhafte Versichertengruppen führen. Die Studie stellt heraus, welche Anreize zur regionalen Risikoselektion unter einem differenzierten Risikostrukturausgleichssystem vorliegen und gibt einen Einblick in die Bedeutung des Problemfeldes. Hierfür werden regionale Versichertenstrukturen gegenüber ihrem geographischen Risikopotential mittels räumlicher Autokorrelationsanalyse untersucht.

Individual decision-making and the generation of medical demand are crucial subjects in healthcare economics. The following scientific discussion can be classified into these threads. The demand for health care services is typically connected to characteristic imperfections reflecting a bias between an objective and a subjective assessment of a specific demand situation or externally caused frictions. For that reason, the realized demand is not necessarily connected to an efficient allocation of resources. Hence, it is a crucial objective to analyze individual decision-making related on the one hand to specific treatment alternatives and on the other hand to the specified decision framework. This framework is characterized by both the attributes of the individual as well as by the external conditions in which the decision takes place. Theirby, the analysis focuses specifically on potential sources of demand inefficiency and their effectiveness. The following discussion broach the issue of two significant objectives within health economics: 1. Trade in medical care and patient migration, 2. Patient autonomy and education. Both fields find their analytical basis in a micro-economic discussion of individual decision behavior. The first field analyzes the decision between medical provision at home or abroad. This subject is specifically related to a potential efficiency gain due to the existence of cross-border price and quality gradients, usually a source of gains in trade. In the focus of the analysis is the impact of the specific characteristics of these gradients as determinants of cross-border medical demand. The second field discusses the investment decision in measures of patient education and prevention in a framework of a common consultation and self-care as imperfect treatment alternatives due to imperfect competences of self-diagnosis and medical self-supply. This subject is related to the commonly acknowledged positive correlation between health and education. Education is able to improve the quality of health production and, therefore, has a specific impact with respect to increasing autonomous behavior of the individual in issues of health production. The specific environment of these decisions significantly influences the mechanism of decision-making and the final outcome; this must be assessed according to the effect on the allocative efficiency of medical demand. The role of price and quality gradients between alternatives, the differentiation of illnesses, as well as subjective factors, are crucial to the results. Moreover, the individual's ability to appraise his or her own health stock and demand decisions is itself risky. Therefore, the form of the insurance coverage is another important element when analyzing individual decisions. The following discussion will clarify the decision-making mechanisms and their impact on efficient resource allocation. Since the focus is on demand behavior, the interaction with, and therefore the behavior of, the supply side is not explicitly formulated.

Die Palliativversorgung (PV) verfolgt das Ziel, die Lebensqualität in der noch verbleibenden Lebenszeit von Patienten mit lebensbedrohlichen Erkrankungen zu maximieren. Deutschland verfügt mittlerweile über ein umfassendes PV-System im ambulanten und stationären Sektor und einen Anspruch auf PV als Teil der Regelversorgung. Im Rahmen dieser Untersuchung soll überprüft werden, ob die Inanspruchnahme der PV-Institutionen der vom Gesetzgeber und medizinischen Experten intendierten Reihenfolge entspricht und ob es Überlebenszeit- oder Gesundheitsausgabenunterschiede gibt (jeweils im Vergleich zu Nicht-Palliativpatienten). Dazu wird ein Paneldatensatz der AOK PLUS (Sachsen und Thüringen) mit Patienten verwendet, die zwischen 2009 und 2012 an einer Krebserkrankung litten (n=447.191). PV-Patienten werden entsprechend ihres Inanspruchnahmeverhaltens in vier Interventionsgruppen eingeteilt, von denen jeder mittels Propensity Score Matchings eine eigene Kontrollgruppe zugeordnet wird. Als statistische Werkzeuge werden v.a. Übergangswahrscheinlichkeiten, Kaplan-Meier-Überlebensfunktionen sowie lineare und nicht-lineare Regressionsmodelle verwendet. Die Ergebnisse legen nahe, dass die Reihenfolge der Inanspruchnahme im Einklang mit Gesetzen und Richtlinien ist. Überlebensnachteile der PV-Patienten können nicht festgestellt werden. Die Gesundheitsausgaben steigen nach erstmaliger Inanspruchnahme einer PV-Institution in allen Stichproben stark an. Dieses Ergebnis ist sehr robust gegenüber Änderungen der Modellspezifikation, des Modelltyps und der Stichprobe. Die Ergebnisse lassen auf eine hohe Struktur- und Prozessqualität der PV-Angebote schließen. Jedoch führt die Inanspruchnahme von PV in ihrer derzeitigen Form offenbar nicht zu Einsparungen. Ein weiterer Ausbau des PV-Systems finanziert sich demnach nicht „von selbst“. Aufgrund der sehr kurzen Nachbetrachtungszeiträume bleibt die gesundheitsökonomische Analyse der PV weiterhin herausfordernd. / Palliative Care (PC) is an approach for patients with life-threatening diseases that focuses on improving quality of life rather than maximizing the remaining life time. Meanwhile, Germany possesses a comprehensive PC system in the ambulatory and inpatient sector where PC treatments are part of standard care. The objective of this research is to evaluate whether patients are using PC institutions as intended by law and medical experts and whether PC patients differ in terms of survival time or health care expenditures (HCE) in comparison to non-PC patients. For this purpose, a panel data set from the statutory health insurance AOK PLUS (covering the German federal states Thuringia and Saxony) is used, that includes all deceased cancer patients between 2009 and 2012 (n=447,191). According to their usage of PC institutions, PC patients were grouped into four different intervention groups and thus each of them was paired with a control group derived from a propensity score matching. A variety of statistical tools has been used, e.g. transition probabilities, Kaplan-Meier survival functions as well as linear and non-linear regression models. Results show that the intended sequences of PC usage are in accordance with law and medical guidelines. There are no disadvantages in survival of PC patients. In all four samples, HCE of PC patients are higher after the initial contact with a PC institution. This result is very robust against adjustments to the model specification, the model type and the sample. Results suggest that structural and process quality of PC is high. However, no saving effect can be identified for PC in its current form. A further extension of the PC system is therefore not “self-financing”. Due to the very short post treatment time, health economic analysis of PC remains challenging.